This textbook is designed for students in statistics, data science, biostatistics, engineering, and physical science programs who need a solid course in the fundamental concepts, methods and theory of statistics to understand, use, and build on modern statistical techniques for complex problems. Examples and exercises incorporate data and R code.
This textbook is designed for students in statistics, data science, biostatistics, engineering, and physical science programs who need a solid course in the fundamental concepts, methods and theory of statistics to understand, use, and build on modern statistical techniques for complex problems. Examples and exercises incorporate data and R code.
Steven E. Rigdon is Professor of Biostatistics at Saint Louis University. He is a fellow of the American Statistical Association and is the author of Statistical Methods for the Reliability of Repairable Systems Calculus, 8th and 9th editions, Monitoring the Health of Populations by Tracking Disease Outbreaks (2020), and Design of Experiments for Reliability Achievement (2022). He has received the Waldo Vizeau Award for technical contributions to quality, the Soren Bisgaard Award, and the Paul Simon Award for linking teaching and research. He is also Distinguished Research Professor Emeritus at Southern Illinois University Edwardsville.
Inhaltsangabe
Part I. Descriptive Statistics & Data Science: 1. Introduction 2. Descriptive statistics 3. Data visualization Part II. Probability: 4. Basic probability 5. Random variables 6. Discrete distributions 7. Continuous distribution Part III. Classical Statistical Inference: 8. About data & data collection 9. Sampling distributions 10. Point estimation 11. Confidence intervals 12. Hypothesis testing 13. Hypothesis tests for two or more samples 14. Hypothesis tests for discrete data 15. Regression Part IV. Bayesian and Other Computer Intensive Methods: 16. Bayesian methods 17. Time series methods 18. The jackknife and bootstrap Part V. Advanced Topics in Inference & Data Science: 19. Generalized linear models and regression trees 20. Cross-validation and estimates of prediction error 21. Large-scale hypothesis testing and the false discovery rate Appendix. More About R.
Part I. Descriptive Statistics & Data Science: 1. Introduction 2. Descriptive statistics 3. Data visualization Part II. Probability: 4. Basic probability 5. Random variables 6. Discrete distributions 7. Continuous distribution Part III. Classical Statistical Inference: 8. About data & data collection 9. Sampling distributions 10. Point estimation 11. Confidence intervals 12. Hypothesis testing 13. Hypothesis tests for two or more samples 14. Hypothesis tests for discrete data 15. Regression Part IV. Bayesian and Other Computer Intensive Methods: 16. Bayesian methods 17. Time series methods 18. The jackknife and bootstrap Part V. Advanced Topics in Inference & Data Science: 19. Generalized linear models and regression trees 20. Cross-validation and estimates of prediction error 21. Large-scale hypothesis testing and the false discovery rate Appendix. More About R.
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